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• W2404907442 abstract "Extracting useful patterns from large network datasets has become a fundamental challenge in many domains. We present Visage, an interactive visual graph querying approach that empowers users to construct expressive queries, without writing complex code (e.g., finding money laundering rings of bankers and business owners). Our contributions are as follows: (1) we introduce graph autocomplete, an interactive approach that guides users to construct and refine queries, preventing over-specification; (2) Visage guides the construction of graph queries using a data-driven approach, enabling users to specify queries with varying levels of specificity, from concrete and detailed (e.g., query by example), to abstract (e.g., with wildcard nodes of any types), to purely structural matching; (3) a twelve-participant, within-subject user study demonstrates Visage's ease of use and the ability to construct graph queries significantly faster than using a conventional query language; (4) Visage works on real graphs with over 468K edges, achieving sub-second response times for common queries." @default.
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• W2404907442 date "2016-06-07" @default.
• W2404907442 modified "2023-10-16" @default.
• W2404907442 title "VISAGE" @default.
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• W2404907442 doi "https://doi.org/10.1145/2909132.2909246" @default.
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